1. Field of the Invention
The present disclosure generally relates to data mining and, more specifically, to methods and systems for regressively clustering and classifying a dataset.
2. Background Information
With the increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain, the need to efficiently and accurately analyze data is increasing. The term “data mining” may be used to describe such an analysis of data and may be referred to herein as the process of identifying and interpreting patterns in databases. Quick and accurate data mining may offer a variety of benefits for applications in which data is accumulated. For example, a better understanding of demand curves within a market may help a business to design multiple models of a product family for different segments of the market. Similarly, the design of marketing campaigns and purchase incentive offerings may be more effective when employed for a proper segmentation of customers, rather than being blindly presented to all customers.
In some cases, predicting values of parameters which are associated with a dataset may be useful. For example, forecasting future product sales from a customer survey may aid in determining production schedules and sale goals. In field of meteorology, forecasting weather from a collection of atmospheric data may be useful. A plurality of other applications may make use of predicting values from a dataset as well. In some cases, the process of making predictions may be dependent on the information obtained from the data mining process. In particular, the process of making predictions may, in some embodiments, involve determining directives by which to classify data into a dataset based upon information mined from the dataset. In cases in which data collection is insufficiently controlled within a dataset, the process of making predictions may be further complicated. In particular, the accuracy of making predictions may be difficult when data is partially labeled or is incomplete within a dataset.
It would, therefore, be advantageous to develop systems and methods for mining and classifying datasets. In addition, it would be beneficial to develop systems and methods for predicting values of parameters which are associated with datasets, particularly values with a relatively small uncertainty. In some cases, the systems and method may be particularly suited for a dataset having a mixture of relationships characterizing its variable parameters.